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Quantocracy’s Daily Wrap for 04/29/2022

This is a summary of links featured on Quantocracy on Friday, 04/29/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Absorption Ratio: Measuring Financial Risk, Part 2 [Portfolio Optimizer]

    In the previous post, I reviewed the turbulence index, an indicator of financial market stress periods based on the Mahalanobis distance, introduced by Chow and al.1 and Kritzman and Li2. In this post, I will review the absorption ratio, a measure of financial market fragility based on principal components analysis, introduced by Kritzman and al.3. I will also show how to compute this absorption
  • How Does Weighting Scheme Impacts Systematic Equity Portfolios? [Quantpedia]

    How often do you think about the weights of the assets in your portfolio? Do you weigh your assets equally, or do you prefer value-weighting? The researchers behind a recent research paper analyzed various weighting schemes and examined their effect on factor strategy return. They studied five weighting schemes that ignore prices: equal weighting, rank weighting, z-score weighting, inverse
  • Betting Against Beta: New Insights [Alpha Architect]

    The 2014 study by Andrea Frazzini and Lasse Pedersen, Betting Against Beta, established strong support for low-beta (as well as low-volatility) strategies. The authors found that for U.S. stocks, the betting against beta (BAB) factor (a portfolio that holds low-beta assets leveraged to a beta of 1 and shorts high-beta assets deleveraged to a beta of 1) realized a Sharpe ratio of 0.78 between
  • Random Forest on Financial Ratios as an Investment Strategy [Quant Dare]

    Random Forests are widely used Machine Learning algorithms. In finance, certain financial ratios are used to try and predict whether or not a company will outperform the market. Can we use the random forest on financial ratios to articulate an investment strategy which outperforms a buy and hold strategy? Thesis on financial ratios In previous posts, we have seen how certain financial ratios

Quantocracy’s Daily Wrap for 04/26/2022

This is a summary of links featured on Quantocracy on Tuesday, 04/26/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Crypto Tokens and Crypto Coins: What Drives Performance? [Factor Research]

    Investors assume that token prices increase with product utilization in the token ecosystem However, the correlation between the token prices and token volumes has been zero Likely explained by token prices substantially being driven by speculation INTRODUCTION Much of the crypto world is, by definition, cryptic and difficult to understand. But two crypto trends are crystal clear: Both talent and
  • Inflation Deep Dive: An Examination of the Underlying BEA PCE Data [Light Finance]

    Inflation is perhaps the least well understood phenomenon in economics. Once said to be exclusively a monetary phenomenon, our current predicament is significantly more complicated and there is little consensus as to the root cause. Until recently the concern was that inflation would run permanently too low. The topic garnered little interest from the public but has seen a sharp reversal in recent

Quantocracy’s Daily Wrap for 04/24/2022

This is a summary of links featured on Quantocracy on Sunday, 04/24/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Hacking 1-Minute Crypto Candlesticks: (2) Custom Charts using Plotly [Quant At Risk]

    A clear and informative time-series visualisation is often a challenge. Especially this is true when it comes to candlestick charts in Python. Searching the Web for a perfect solution may bring you to the Plotly package which stores in its arsenal the corresponding function allowing for easy charting (see some examples here). The candlestick chart is a popular way to present the traded assets
  • The Price of Transaction Costs [Quantpedia]

    Capturing the systematic premia is the main aim of many quantitative traders. However, investors tend to overlook an important factor when backtesting. Trading costs are an essential part of every trade, and yet even when we consider them, we often only use an approximation. The recent article from Angana Jacob (SigTech) looks into how heavily trading costs affect the overall return of various

Quantocracy’s Daily Wrap for 04/21/2022

This is a summary of links featured on Quantocracy on Thursday, 04/21/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Measuring uncertainty in time series data [Quant Dare]

    In financial time series it is very common to make predictions of single points such as expected future prices or returns. But is there any other way of adding more information in our forecasts? In todays post we will be making probabilistic forecasts for time series data using recurrent neural networks with pytorch. Introduction Even though point forecasting gives us information about
  • The Implementation Costs of Indexed ETFs [Alpha Architect]

    A common mistake made by many passive investors is that they view all index funds in the same asset class as commodities(1), often considering only the expense ratio when making their investment choices. However, not all index funds are alike, and not all passively managed funds (what I refer to as systematically structured portfolios) are index funds. Index funds and systematically
  • Can Market Maker Capital Constraints Result in Mispricing of ETFs? [Alpha Architect]

    In this research, the authors explore the role of financial intermediaries in contagion or comovements in pricing efficiency. Specifically, lead market makers (LMMs) like Goldman Sachs, Cantor Fitzgerald, RBC Capital Markets, and others, have funding constraints that may influence their ability to accurately price ETFs and cause contagion in the pricing of financial assets. The market for ETFs
  • Thematic Indices: Looking at the Past or the Future? [Factor Research]

    Thematic indices from MSCI have outperformed their benchmark since 2018 However, they have a rather unattractive factor mix Going against decades of research is not a sound investment strategy INTRODUCTION Although much of the future is uncertain, some technological innovations are rather certain. At some point, we will all have self-driving cars, robots that help us with daily chores, can travel

Quantocracy’s Daily Wrap for 04/18/2022

This is a summary of links featured on Quantocracy on Monday, 04/18/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Government Bonds Have Failed to Deliver When Needed [Allocate Smartly]

    Most government bond funds have suffered major losses this year. What is worse is that those major losses have come when theyre needed most, when stocks and other risk assets are also falling. During times of market stress, gov bonds tend to act as a counterbalance to risk assets, but so far this year theyve failed to deliver when needed. Data dump: It has been 71 trading days since the
  • The Turbulence Index: Measuring Financial Risk [Portfolio Optimizer]

    One of the challenges in portfolio management is the timely detection of financial market stress periods, typically characterized by an increase in volatility and a breakdown in asset correlations1. Chow and al.2 propose to detect such periods through the usage of the caste distance, a measure initially introduced by Mahalanobis34 to classify human skulls in India and now commonly called the
  • How to use FX carry in trading strategies [SR SV]

    FX forward-implied carry is a valid basis for trading strategies because it is related to divergences in monetary and financial conditions. However, nominal carry is a cheap and rough indicator: related PnLs are highly seasonal, sensitive to global equity markets, and prone to large drawdowns. Simple alternative concepts such as real carry, interest rate differentials, and volatility-adjusted
  • Research Review | 15 April 2022 | Risk Factor Premia [Capital Spectator]

    A Look Under the Hood of Momentum Funds Ayelen Banegas and Carlo Rosa (Federal Reserve) February 2022 Momentum investing has surged over the past few years, with assets growing at three times the rate of conventional funds. Using a comprehensive dataset of US equity funds, this paper examines the economic value of momentum funds. Overall, we find that risk-adjusted returns of momentum funds are,

Quantocracy’s Daily Wrap for 04/14/2022

This is a summary of links featured on Quantocracy on Thursday, 04/14/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trading and investing performance: year eight [Investment Idiocy]

    Eight years! Wow. In late 2013 I walked out of an office for the last time where I had been working for AHL, a large systematic futures trading fund. A few months later, in April 2014, I had my own very small systematic futures trading account, and I started doing these performance reviews. And this is my eighth review. Double wow! As usual these cover the UK tax year, in this case from April 6th
  • Bond Investing in Inflationary Times [Alpha Architect]

    As the chief research officer of Buckingham Strategic Partners, the issue I am being asked to address most often is about fixed income strategies when yields are at historically low levels and inflation risk is heightened due to the unprecedented increase in money creation (through quantitative easing), the extraordinary expansionary fiscal spending around the globe, and the war in Ukraine driving
  • Never Sell in May! [Financial Hacker]

    Sell in May and go away is an old stock traders wisdom. But in his TASC May 2022 article, Markos Katsanos examined that rule in detail and found that it should rather be Sell in August and buy back in October. Can trading be really this easy? Lets have a look at the simple seasonal trading rule and a far more complex application of it. The trading algorithm Sell in August and

Quantocracy’s Daily Wrap for 04/13/2022

This is a summary of links featured on Quantocracy on Wednesday, 04/13/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What’s the Best Factor for High Inflation Periods? – Part II [Quantpedia]

    This second article offers a different look at high inflation periods, which we already analyzed in Whats the Best Factor for High Inflation Periods? Part I. In this second part, we look at factor performance during 10-year periods of high inflation. High Inflation Periods As we already outlined in the first part, inflation measures an increase in prices over time. Most typically, we

Quantocracy’s Daily Wrap for 04/12/2022

This is a summary of links featured on Quantocracy on Tuesday, 04/12/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • An Introduction to Stooq Pricing Data [Quant Start]

    In the previous article we learnt how to setup a prototyping environment for algorithmic trading using Jupyter Notebooks. We used Yahoo data with Pandas DataReader. In this article we will be looking at another free market data provider Stooq. If you would like to follow along with the tutorial and do not have the protoyping environment set up you will need: Jupyter v1.0 Pandas v1.4

Quantocracy’s Daily Wrap for 04/11/2022

This is a summary of links featured on Quantocracy on Monday, 04/11/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • What’s the Best Factor for High Inflation Periods? – Part I [Quantpedia]

    In the past couple of weeks, we have done a few event studies, analyzing events that in one way or another resemble what is happening in the world today. At the beginning of March, we examined Factor Performance in Cold War Crises, and at the end of March, we brought you an article analyzing Nuclear Threats and Factor Performance. Today we are going to look into factor performance during high
  • Trend Following & Factor Investing – Unexpected Cousins? [Factor Research]

    Trend following and beta-neutral factor investing are considered diversifying strategies However, since 2009 their correlations to stocks moved in tandem Both strategies had related performance drivers and risk exposures INTRODUCTION Asset classes seem easy to distinguish at first. For example, stocks and corporate bonds provide different exposure to the capital structure of companies. However,
  • Find Your Best Market to Trade With the Hurst Exponent [Raposa Trade]

    After five consecutive years of drought, Northern Californians welcomed the heavy rainfall in the winter of 2016-2017. By February, however, the rain had led many to worry about the integrity of the Lake Oroville Dam. Officials evacuated over 200,000 residents who lived downstream of the dam along the Feather River and engineers opened the emergency spillway. Soon, however, a small crack in the
  • Shorting ETFs: A look into the ETF Loan Market [Alpha Architect]

    The growth of ETFs has been explosive (and we arent helping the matter via ETF Architect which facilitates low-cost high quality ETF white label services). At the end of 2020, there was roughly $5.4 trillion invested in ETFs in the United States, representing more than 25% of US market trading by daily volume in the recent decade (ICI Factbook, 2021). One of the many benefits of ETFs relative

Quantocracy’s Daily Wrap for 04/08/2022

This is a summary of links featured on Quantocracy on Friday, 04/08/2022. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Guide to Obtaining Time Series Datasets in Python (h/t @PyQuantNews) [Machine Learning Mastery]

    Datasets from real-world scenarios are important for building and testing machine learning models. You may just want to have some data to experiment with an algorithm. You may also want to evaluate your model by setting up a benchmark or determining its weaknesses using different sets of data. Sometimes, you may also want to create synthetic datasets, where you can test your algorithms under
  • Is Sector-neutrality in Factor Investing a Mistake? [Alpha Architect]

    Firm characteristics such as size, book-to-market ratio, profitability, and momentum have been found to be correlated with expected returns. The predictive power of these characteristics may stem from their industry component, their firm-specific component, or both. For example, while the study Do Industries Explain Momentum, found that momentum in stocks stems from the industry component,
  • Simple Machine Learning Models on OrderBook/PositionBook Features [Dekalog Blog]

    This post is about using OrderBook/PositionBook features as input to simple machine learning models after previous investigation into the relevance of such features. Due to the amount of training data available I decided to look only at a linear model and small neural networks (NN) with a single hidden layer with up to 6 hidden neurons. This choice was motivated by an academic paper I read online